It is typical for a company in the agricultural seed industry to generate one or more research plots in order to evaluate certain seed varieties. Such seed varieties may include, but need not be limited to, seeds from a specific source, genotype, population, and/or breeding line that may or may not contain varying constructs or events of single or stacked transgenes. In such a manner, researchers may evaluate characteristics of the plants growing in the research plot, as well as characteristics of any crops produced from the plants. Examples of desirable traits include, but are not limited to, increased yield, increased homozygosity, improved or newly conferred resistance and/or tolerance to specific herbicides and/or pests and pathogens, increased oil content, altered starch content, agronomic traits pertaining to standability, nutraceutical composition, stress tolerance, and specific morphological based trait enhancements. In the context of ears of corn, increased kernel quantity, quality, size, and density on the ear of corn may all be desirable. In some instances these characteristics may be compared to plants grown from different seed varieties in the same research plot or a separate research plot.
Thus, certain experiments may require a researcher to record and evaluate the characteristics of crops grown from a variety of different genotypic backgrounds. Traditional research plot evaluation may be performed using an automated harvesting system (i.e. combine harvester). However, this technology may provide a uni-dimensional view of yield in as much as it may provide a single bulked weight of all grains harvested from that plot with no information on variation within the plot, no removal of alley-adjacent ears, and/or no quantification of ear flex and no information on yield component traits. As such, researchers may resort to manual evaluation of those traits, which may require significant time and labor in order to evaluate a statistically significant number of samples of ears of corn. Further, manual methods of evaluating sample crops may suffer from human error. In this regard, manually recording data relating the samples presents the opportunity for errors to occur. Further, it may be possible for the workers to lose track of which sample crops relate to which variety of seed. For example, in some instances sample crops from different seed varieties may appear substantially identical to the naked eye and thus may not be easily linked to the corresponding planted seed variety. Accordingly, known methods of collecting and evaluating crop samples may require significant manual labor and may produce erroneous labor due to the potential for human error to exist.